add Label Relaxation

dicee.egg-info/SOURCES.txt

@@ -0,0 +1,95 @@
+LICENSE
+MANIFEST.in
+README.md
+requirements.txt
+setup.py
+dicee/__init__.py
+dicee/__main__.py
+dicee/abstracts.py
+dicee/analyse_experiments.py
+dicee/callbacks.py
+dicee/config.py
+dicee/dataset_classes.py
+dicee/eval_static_funcs.py
+dicee/evaluator.py
+dicee/executer.py
+dicee/knowledge_graph.py
+dicee/knowledge_graph_embeddings.py
+dicee/lp.png
+dicee/query_generator.py
+dicee/sanity_checkers.py
+dicee/static_funcs.py
+dicee/static_funcs_training.py
+dicee/static_preprocess_funcs.py
+dicee.egg-info/PKG-INFO
+dicee.egg-info/SOURCES.txt
+dicee.egg-info/dependency_links.txt
+dicee.egg-info/entry_points.txt
+dicee.egg-info/requires.txt
+dicee.egg-info/top_level.txt
+dicee/figures/deploy_qmult_family.png
+dicee/models/__init__.py
+dicee/models/adopt.py
+dicee/models/base_model.py
+dicee/models/clifford.py
+dicee/models/complex.py
+dicee/models/dualE.py
+dicee/models/ensemble.py
+dicee/models/function_space.py
+dicee/models/octonion.py
+dicee/models/pykeen_models.py
+dicee/models/quaternion.py
+dicee/models/real.py
+dicee/models/static_funcs.py
+dicee/models/transformers.py
+dicee/read_preprocess_save_load_kg/__init__.py
+dicee/read_preprocess_save_load_kg/preprocess.py
+dicee/read_preprocess_save_load_kg/read_from_disk.py
+dicee/read_preprocess_save_load_kg/save_load_disk.py
+dicee/read_preprocess_save_load_kg/util.py
+dicee/scripts/__init__.py
+dicee/scripts/index_serve.py
+dicee/scripts/run.py
+dicee/trainer/__init__.py
+dicee/trainer/dice_trainer.py
+dicee/trainer/model_parallelism.py
+dicee/trainer/torch_trainer.py
+dicee/trainer/torch_trainer_ddp.py
+tests/__init__.py
+tests/test_adaptive_swa.py
+tests/test_answer_multi_hop_query.py
+tests/test_auto_batch_finder.py
+tests/test_continual_training.py
+tests/test_custom_trainer.py
+tests/test_deployment.py
+tests/test_different_backends.py
+tests/test_download_and_eval.py
+tests/test_ensemble_construction.py
+tests/test_execute_start.py
+tests/test_gradient_accumulation.py
+tests/test_inductive_regression.py
+tests/test_k_fold_cv_1_vs_all.py
+tests/test_k_fold_cv_k_vs_all.py
+tests/test_k_fold_cv_neg_sample.py
+tests/test_large_kg.py
+tests/test_link_prediction_evaluation.py
+tests/test_onevssample.py
+tests/test_online_learning.py
+tests/test_pickle.py
+tests/test_pykeen.py
+tests/test_read_few_only.py
+tests/test_regression_DualE.py
+tests/test_regression_aconex.py
+tests/test_regression_all_vs_all.py
+tests/test_regression_cl.py
+tests/test_regression_clifford.py
+tests/test_regression_complex.py
+tests/test_regression_conex.py
+tests/test_regression_convo.py
+tests/test_regression_distmult.py
+tests/test_regression_model_paralelisim.py
+tests/test_regression_omult.py
+tests/test_regression_pyke.py
+tests/test_regression_qmult.py
+tests/test_saving_embeddings.py
+tests/test_trainers.py

dicee.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

dicee.egg-info/entry_points.txt

@@ -0,0 +1,3 @@
+[console_scripts]
+dicee = dicee.scripts.run:main
+dicee_vector_db = dicee.scripts.index_serve:main

dicee.egg-info/requires.txt

@@ -0,0 +1,40 @@
+pandas>=2.1.0
+polars==1.9.0
+pyarrow>=11.0.0
+rdflib>=7.0.0
+torch>=2.5.1
+lightning>=2.1.3
+tiktoken>=0.5.1
+psutil>=5.9.4
+matplotlib>=3.8.2
+pykeen>=1.10.2
+numpy==1.26.4
+
+[dev]
+pandas>=2.1.0
+polars==1.9.0
+pyarrow>=11.0.0
+rdflib>=7.0.0
+torch>=2.5.1
+lightning>=2.1.3
+tiktoken>=0.5.1
+psutil>=5.9.4
+matplotlib>=3.8.2
+pykeen>=1.10.2
+numpy==1.26.4
+ruff>=0.0.284
+pytest>=7.2.2
+scikit-learn>=1.2.2
+
+[min]
+pandas>=2.1.0
+polars==1.9.0
+pyarrow>=11.0.0
+rdflib>=7.0.0
+torch>=2.5.1
+lightning>=2.1.3
+tiktoken>=0.5.1
+psutil>=5.9.4
+matplotlib>=3.8.2
+pykeen>=1.10.2
+numpy==1.26.4

dicee.egg-info/top_level.txt

@@ -0,0 +1,2 @@
+dicee
+tests

dicee/models/base_model.py

@@ -6,6 +6,38 @@
 from torch.nn import functional as F
 from .adopt import ADOPT
 
+class LabelRelaxationLoss(nn.Module):
+    def __init__(self, alpha=0.1, dim=-1, logits_provided=True, one_hot_encode_trgts=False, num_classes=-1):
+        super(LabelRelaxationLoss, self).__init__()
+        self.alpha = alpha
+        self.dim = dim
+        self.gz_threshold = 0.1
+        self.logits_provided = logits_provided
+        self.one_hot_encode_trgts = one_hot_encode_trgts
+        self.num_classes = num_classes
+
+    def forward(self, pred, target):
+        if self.logits_provided:
+            pred = pred.softmax(dim=self.dim)
+
+        # Apply one-hot encoding to targets
+        if self.one_hot_encode_trgts:
+            target = F.one_hot(target, num_classes=self.num_classes)
+
+        # Construct credal set
+        with torch.no_grad():
+            sum_y_hat_prime = torch.sum((torch.ones_like(target) - target) * pred, dim=-1)
+            pred_hat = self.alpha * pred / torch.unsqueeze(sum_y_hat_prime, dim=-1)
+            target_credal = torch.where(target > self.gz_threshold, torch.ones_like(target) - self.alpha, pred_hat)
+
+        # Calculate divergence
+        divergence = torch.sum(F.kl_div(pred.log(), target_credal, log_target=False, reduction="none"), dim=-1)
+
+        pred = torch.sum(pred * target, dim=-1)
+
+        result = torch.where(torch.gt(pred, 1. - self.alpha), torch.zeros_like(divergence), divergence)
+        return torch.mean(result)
+
 class BaseKGELightning(pl.LightningModule):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -135,7 +167,10 @@ def __init__(self, args: dict):
         self.kernel_size = None
         self.num_of_output_channels = None
         self.weight_decay = None
+
         self.loss = torch.nn.BCEWithLogitsLoss()
+        #self.loss = LabelRelaxationLoss()
+
         self.selected_optimizer = None
         self.normalizer_class = None
         self.normalize_head_entity_embeddings = IdentityClass()